A hybrid model-data method for seismic response reconstruction of instrumented buildings

This study presents a two-step hybrid (model-data fusion) method for reconstructing the seismic response of instrumented buildings at their non-instrumented floors. Over the past couple of decades, seismic data recorded within instrumented buildings have yielded invaluable insights into the behavior of civil structures, which were arguably impossible to obtain through numerical simulations, laboratory-scale experiments, or even in-situ testing. Recently, advances in sensing technology have opened new pathways for structural health monitoring (SHM) and rapid post-earthquake assessment. However, data-driven techniques tend to lack accuracy when structures have sparse instrumentation. In addition, creating detailed numerical models for the monitored structures is labor-intensive and time-consuming, often unsuitable for rapid post-event assessments. The common approach to address these challenges has been to use simple interpolation techniques over the sparse measurements. However, uncertainties associated with such estimates are usually overlooked, and these methods have certain physical limitations. In this study, we propose a two-step approach for reconstructing seismic responses. In the initial step, a coupled shear–flexural beam model is calibrated using data collected from instrumented floors. Next, the residual, representing the difference between measurements and the beam model’s predictions, is used to train a Gaussian process regression model. The combination of these two models provides both the mean and variance of the response at the non-instrumented floors. This new approach is verified by using simulated acceleration responses of a tall building. Validation is attained by using real seismic data recorded in two tall buildings and comparing the method’s predictions with actual measurements on floors not used for training. Finally, data recorded in a 52-story building during multiple earthquakes are used for demonstrating the practical application of the proposed approach in real-world scenarios.